Abstract #3071

Retrospective quantification pharmacokinetics of clinical breast DCE-MRI using deep learning

Chaowei Wu^1,2, Lixia Wang¹, Nan Wang^1,3, Stephen Pandol⁴, Anthony G Christodoulou^1,2, Yibin Xie¹, and Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Radiology Department, Stanford University, Stanford, CA, United States, ⁴Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, DSC & DCE PerfusionStandard-of-care DCE-MRI suffers from a limited number of contrast phases and low temporal resolution, preventing the quantification of pharmacokinetic parameters. Quantitative DCE-MRI techniques have not yet been widely applied in the clinic due to the limited availability of specialized sequences and image reconstruction. To tackle this problem, we proposed to improve the temporal resolution of multi-phasic DCE-MRI by deep learning post-processing and demonstrated promising results in tumor delineation in the Duke-Breast-Cancer-MRI dataset.

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